Path Planning for Underwater Inspection Robots Based on Improved Ant Colony Algorithm

SHEN Zhaowen; PAN Xinfeng; HUANG Mengshi; YANG Tianci; ZHANG Wenze; YI Zhengyao

doi:10.11993/j.issn.2096-3920.2026-0066

Volume 34 Issue 5

Oct 2026

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Article Navigation > Journal of Unmanned Undersea Systems > 2026 > 34(5): 930-938

SHEN Zhaowen, PAN Xinfeng, HUANG Mengshi, YANG Tianci, ZHANG Wenze, YI Zhengyao. Path Planning for Underwater Inspection Robots Based on Improved Ant Colony Algorithm[J]. Journal of Unmanned Undersea Systems, 2026, 34(5): 930-938. doi: 10.11993/j.issn.2096-3920.2026-0066

Citation:

SHEN Zhaowen, PAN Xinfeng, HUANG Mengshi, YANG Tianci, ZHANG Wenze, YI Zhengyao. Path Planning for Underwater Inspection Robots Based on Improved Ant Colony Algorithm[J]. Journal of Unmanned Undersea Systems, 2026, 34(5): 930-938. doi: 10.11993/j.issn.2096-3920.2026-0066

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Path Planning for Underwater Inspection Robots Based on Improved Ant Colony Algorithm

doi: 10.11993/j.issn.2096-3920.2026-0066

1.
Navigation and Engineering College, Dalian Ocean University, Dalian 110623, China
2.
The 760 Research Institute, China State Shipbuilding Corporation limited, Dalian 116013, China

Received Date: 2026-04-02
Accepted Date: 2026-05-11
Rev Recd Date: 2026-05-04

Available Online: 2026-09-04

Abstract

Abstract

When the traditional ant colony algorithm is applied in path planning of underwater inspection robots, issues occur, including slow convergence speed, susceptibility to local optima, and poor adaptability to the underwater environment. In response to these issues, an improved ant colony algorithm was proposed. Firstly, a comprehensive evaluation function considering path length, safety, smoothness, and water flow adaptability was constructed. The pheromone release coefficient was dynamically adjusted by the path fitness. The pheromone volatilization coefficient was also dynamically adjusted according to the iterative process, thus expediting the convergence speed in the later stage of the algorithm iteration. Secondly, the water flow factor and obstacle distance factor were introduced into the heuristic function to guide the algorithm to choose the superior path. Then, the simulated annealing mechanism was integrated to avoid the algorithm falling into a local optimum. Finally, the B-spline curve was applied to smooth the path to reduce the energy consumption of the underwater robots. A comparison experiment was carried out under the grid simulation environments of 20×20 and 30×30, respectively. The results show that compared to the traditional ant colony algorithm and reference algorithm, the proposed improved ant colony algorithm achieves a shorter planning path, fewer turning points, and faster convergence speed, which effectively meets the actual requirements of collisionless and low-energy-consumption planning path for underwater inspection robots
- path planning,
- underwater inspection robot,
- ant colony algorithm,
- simulated annealing mechanism

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References(17)

References

[1]	陈永康, 蒲德奎, 何小丽. 基于PSO融合蚁群算法的机器人路径规划研究[J]. 重庆电力高等专科学校学报, 2024, 29(6): 20-24. doi: 10.3969/j.issn.1008-8032.2024.06.006
[2]	吕诗为, 朱迎谷, 卢倪斌, 等. 基于改进粒子群算法的水下机器人路径规划研究[J]. 控制与信息技术, 2023(6): 58-64. doi: 10.13889/j.issn.2096-5427.2023.06.009 Lü S W, Zhu Y G, Lu N B, et al. Research on path planning of underwater robots based on improved particle swarm optimization algorithm[J]. Control and Information Technology, 2023(6): 58-64. doi: 10.13889/j.issn.2096-5427.2023.06.009
[3]	姚艳杰, 李一卓, 衣正尧, 等. 基于改进人工势场法的水下机器人路径规划[J]. 船舶工程, 2025, 47(4): 1-10. doi: 10.13788/j.cnki.cbgc.2025.04.01 Yao Y J, Li Y Z, Yi Z Y, et al. Path planning of underwater robot based on improved artificial potential field method[J]. Ship Engineering, 2025, 47(4): 1-10. doi: 10.13788/j.cnki.cbgc.2025.04.01
[4]	王慧锬, 陈坤, 何丽, 等. 融合改进A算法和人工势场法的机器鱼路径规划[J]. 电子测量技术, 2025, 48(13): 58-72. doi: 10.19651/j.cnki.emt.2517856 Wang H T, Chen K, He L, et al. Path planning of robotic fish combining improved A algorithm and artificial potential field method[J]. Electronic Measurement Technology, 2025, 48(13): 58-72. doi: 10.19651/j.cnki.emt.2517856
[5]	刘志华, 张冉, 郝梦男, 等. 基于改进T分布烟花-粒子群算法的AUV全局路径规划[J]. 电子学报, 2024, 52(9): 3123-3134. doi: 10.12263/DZXB.20230814 Liu Z H, Zhang R, Hao M N, et al. Global path planning of AUV based on improved T-distribution fireworks-particle swarm optimization algorithm[J]. Acta Electronica Sinica, 2024, 52(9): 3123-3134. doi: 10.12263/DZXB.20230814
[6]	钱程. 基于边缘计算的动环监管自适应平台研究[D]. 哈尔滨: 哈尔滨理工大学, 2022.
[7]	胡佳伟, 张佳伊, 裴庆雨. 一种基于改进蚁群算法的无人机路径规划方法[J]. 无线电工程, 2025, 55(10): 2105-2113. doi: 10.3969/j.issn.1003-3106.2025.10.018
[8]	许明乐, 游晓明, 刘升. 基于统计分析的自适应蚁群算法及应用[J]. 计算机应用与软件, 2017, 34(7): 204-211. doi: 10.3969/j.issn.1000-386x.2017.07.038 Xu M L, You X M, Liu S. Adaptive ant colony algorithm based on statistical analysis and its application[J]. Computer Applications and Software, 2017, 34(7): 204-211. doi: 10.3969/j.issn.1000-386x.2017.07.038
[9]	刘颖, 刘为国. 基于改进蚁群算法的机器人路径规划研究[J]. 黑龙江工业学院学报(综合版), 2025, 25(8): 112-118.
[10]	付乐乐, 陈宏, 巩伟杰. 基于改进蚁群算法的水下机器人路径规划[J]. 自动化与仪表, 2022, 37(4): 46-50. doi: 10.19557/j.cnki.1001-9944.2022.04.010 Fu L L, Chen H, Gong W J. Path planning of underwater robot based on improved ant colony algorithm[J]. Automation & Instrumentation, 2022, 37(4): 46-50. doi: 10.19557/j.cnki.1001-9944.2022.04.010
[11]	刘兴盛, 王俊雄. 基于改进蚁群算法的水下机器人路径规划算法[J]. 舰船科学技术, 2022, 44(21): 80-87. doi: 10.3404/j.issn.1672-7649.2022.21.017 Liu X S, Wang J X. Path planning algorithm for underwater robot based on improved ant colony algorithm[J]. Ship Science and Technology, 2022, 44(21): 80-87. doi: 10.3404/j.issn.1672-7649.2022.21.017
[12]	金将, 王小平, 臧铁钢, 等. 基于改进蚁群算法的机器人避障路径规划[J]. 计算机工程与设计, 2025, 46(4): 950-958. doi: 10.16208/j.issn1000-7024.2025.04.002
[13]	张代雨, 杨超翔, 鲍超明, 等. 基于改进融合蚁群A*算法的路径规划方法[J]. 舰船科学技术, 2025, 47(9): 96-101. doi: 10.3404/j.issn.1672-7649.2025.09.017
[14]	林梦成, 薛波, 刘昕宇. 基于改进蚁群算法的焊接机器人路径规划方法[J]. 传感器与微系统, 2025, 44(7): 24-27, 31. doi: 10.13873/J.1000-9787(2025)07-0024-04 Lin M C, Xue B, Liu X Y. Path planning method for welding robot based on improved ant colony algorithm[J]. Transducer and Microsystem Technologies, 2025, 44(7): 24-27, 31. doi: 10.13873/J.1000-9787(2025)07-0024-04
[15]	鲍佳松. 基于改进蚁群算法的无人清舱作业路径规划方法与研究[J]. 苏州科技大学学报(工程技术版), 2026, 39(S1): 15-20.
[16]	郝兆明, 安平娟, 李红岩, 等. 增强目标启发信息蚁群算法的移动机器人路径规划[J]. 科学技术与工程, 2023, 23(22): 9585-9591. doi: 10.3969/j.issn.1671-1815.2023.22.030 Hao Z M, An P J, Li H Y, et al. Mobile robot path planning based on ant colony algorithm with enhanced target heuristic information[J]. Science Technology and Engineering, 2023, 23(22): 9585-9591. doi: 10.3969/j.issn.1671-1815.2023.22.030
[17]	刘璐, 沈小伟, 葛超, 等. 基于改进蚁群算法的植保无人机路径规划[J]. 计算机仿真, 2024, 41(1): 39-43. doi: 10.3969/j.issn.1006-9348.2024.01.009 Liu L, Shen X W, Ge C, et al. Path planning of plant protection UAV based on improved ant colony algorithm[J]. Computer Simulation, 2024, 41(1): 39-43. doi: 10.3969/j.issn.1006-9348.2024.01.009